This is awesome, especially as someone who is new to recruiting in the tech space. Sent to a couple other HR/recruiters and they love your posts!
What I learned from reading 8,000 recruiting messages
11–20 of 26 posts
Re: What I learned from reading 8,000 recruiting messages
#12If I ran a company, I would want to hire this woman.
Re: What I learned from reading 8,000 recruiting messages
#13If I ran a company, I would want to hire this woman.
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Re: What I learned from reading 8,000 recruiting messages
#14Love this. I wish more recruiters were this data-driven.
Since Hired is a marketplace, it's a lot easier for us to analyze data because:
1.) Data is stored in a central database, and not across dozens of email accounts, LinkedIn Recruiter accounts, etc.
2.) Working with over 1100 companies, and hundreds of candidates every week, we have a huge sample size to draw on
3.) We actually have a full time Business Intelligence Analyst on our team, and we subscribe to data analysis tools such as "Looker" to build internal dashboards, and metrics which help us drive marketplace efficiency... we also have a Data Scientist on staff, whom we found on Hired (naturally!)
Re: What I learned from reading 8,000 recruiting messages
#15If I ran a company, I would want to hire this woman.
She's an engineer actually fixing recruiting... What more could you want for a talent acquisition thought leader.hopefully she can bring about more evangelism to recruiting.
Re: What I learned from reading 8,000 recruiting messages
#16As a developer with a fairly interesting background, I've been getting messages from recruiters for years. As Aline's post elucidates, most of the messages are completely impersonal. I had been ignoring these e-mails for years. I then got a message from a recruiter that was really well written and completely geared towards me. I was shocked that someone took the time to actually read my resume. That recruiter got a response from me. He then lined me up with a gig. His approach also got four other people hired at the job he lined me up with.
Re: What I learned from reading 8,000 recruiting messages
#17Not sure I understand; the main variable for initial response is salary yet in none of the examples of initial emails salary is mentioned (that's my experience as well)
Re: What I learned from reading 8,000 recruiting messages
#18Not sure I understand; the main variable for initial response is salary yet in none of the examples of initial emails salary is mentioned (that's my experience as well)
On Hired's platform, each recruiting message is accompanied by a salary offer, separate from the message body.
Re: What I learned from reading 8,000 recruiting messages
#19I hope every developer reads this.
Re: What I learned from reading 8,000 recruiting messages
#20> These values come from running a logistic regression on the factors that were most statistically significant. The y-axis represents the standardized value of the coefficients in our regression. "Most statistically significant" does not imply that the variables themselves are statistically significant, and it does not imply that the logistic regression itself is accurate (especially since the regression uses only 5…
Sorry, you're right, that's unclear. We chose to run a logistic regression on a subset of factors that were statistically significant (i.e. every factor in the graph is significant). In other words, we chose the factors that had the largest effect size and then plugged them into the regression.
so wait -- you ran the regression with lots of factors, and then dropped those you found not to be statistically significant?
I would suggest it's not good practice to drop variables, even if they aren't statistically significant (and what an argument that can become if you test things simultaneously). Particularly if there's any chance they are correlated with other variables. Read Pearl; causality (which is what you're really discussing) is a rat's nest.
Also, when you plot the value of the coefficients, I would suggest ordering them by abs(coef) * stddev(var). This may not matter much for you since it looks like most of your variables are indicator variables, but it's still good practice.
edit: oh, hi, you're the Aline that wrote this? Thanks for the interesting analysis.